Energy Aware Node Selection for Cluster-Based Data Accuracy Estimation in Wireless Sensor Networks

The main objective of this paper is to reduce the number of sensor nodes by estimating a trade off between data accuracy and energy consumption for selecting nodes in probabilistic approach in a distributed network. Design Procedure/Approach: Observed data are highly correlated among sensor nodes in the spatial domain due to deployment of high density of sensor nodes. These sensor nodes form non-overlapping distributed clusters due to high data correlation among them. We develop a probabilistic model for each distributed cluster to perform data accuracy and energy consumption model in the network. Finally we find a trade off between data accuracy and energy consumption model to select an optimal number of sensor nodes in each distributed cluster. We also compare the performance for our data accuracy estimation model with information accuracy model for each distributed cluster in the network. Practical Implementation: Measuring temperature in physical environment and measuring moisture content in agricultural field. Inventive/Novel Idea: Optimal node selection in probabilistic approach using the trade of between data accuracy and energy consumption in a cluster-based distributed network.